This disclosure relates to a method and apparatus for a service oriented architecture that monitors applications in a peer-to-peer fashion. More particularly, this disclosure relates to a method and apparatus for a serverless mechanism that can perform real time analysis and anomaly detection during the operation of software services on a MultiFunction Device (MFD) and/or other devices.
While this disclosure is particularly directed towards serverless distributed monitoring for multifunction devices and thus will be described with specific reference thereto, it will be appreciated that this disclosure may have usefulness in other fields and applications. For example, this disclosure may be useful in providing an architecture for analysis of a plurality of devices including Personal Digital Assistance (PDAs), mobile units, CPUs, etc.
By way of background, current Service Oriented Architectures (SOA) include multifunction device fleets that run several types of services. These services include printing, faxing, scanning, emailing, etc. Needless to say, these services are not without their problems. Sometimes there are anomalies in the system that require supervision in order to detect them. Currently in the art, there are a number of ways in order to detect and monitor these anomalies. One approach includes setting parameters, such as setting the number clusters that must be detected. Other approaches include monitoring the quality of service sensitive resources. However, these prior art approaches generally require a fair amount of human interaction. There is currently no hands-free serverless mechanism that detects anomalies automatically.
Therefore, there is a need in the art for a serverless decentralized overlay mechanism that monitors and detects anomalies in a SOA. It would be desirable for this architecture to combine sets of services that an MFD fleet can provide and internalize the resource needs (such as computing and memory) without mandating additional special purpose hardware tasked with monitoring the fleet. It would further be desirable for this architecture to utilize a variety of monitoring scenarios including fleet health, usage monitoring, and detection of malicious attacks, e.g. Denial of Service (DOS). Moreover, it would be desirable for the architecture to inherently address cost effectiveness and load balancing while spreading the workload among multiple available underutilized MFDs in the fleet. Furthermore, it would be desirable for this architecture to run virtually unsupervised using parameters inherent in the data.
The present disclosure contemplates a new and improved system and method which resolves the above-referenced difficulties and others.